Elicitation in the classical model

Book Chapter (2018)
Author(s)

John Quigley (University of Strathclyde)

Abigail Colson (University of Strathclyde)

W.P. Aspinall (Aspinall and Associates, University of Bristol)

RM Cooke (Resources for the Future, TU Delft - Applied Probability)

Research Group
Applied Probability
DOI related publication
https://doi.org/10.1007/978-3-319-65052-4_2
More Info
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Publication Year
2018
Language
English
Research Group
Applied Probability
Pages (from-to)
15-36
ISBN (print)
978-3-319-65051-7
ISBN (electronic)
978-3-319-65052-4

Abstract

The Classical Model (CM) is a performance-based approach for mathematically aggregating judgements from multiple experts, when reasoning about target questions under uncertainty. Individual expert performance is assessed against a set of seed questions, items from their field, for which the analyst knows or will know the true values, but the experts do not; the experts are, however, expected to provide accurate and informative distributional judgements that capture these values reliably. Performance is measured according to metrics for each expert’s statistical accuracy and informativeness, and the two metrics are convolved to determine a weight for each expert, with which to modulate their contribution when pooling them together for a final combined assessment of the desired target values. This chapter provides mathematical and practical details of the CM, including describing the method for measuring expert performance and discussing approaches for devising good seed questions.

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